Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali

Hendayanti, Ni Putu Nanik; Nurhidayati, Maulida

doi:10.30812/varian.v3i2.668

Cited by 9 publications

(11 citation statements)

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“…The data in this study were sourced from the Central Statistics Agency of Nusa Tenggara West through the page https://ntb.bps.go.id/. The stages of the research carried out are as follows (Hendayanti and Nurhidayati, 2020) The Box-Cox test results in Figure 1 show that the value of λ obtained is 0.22, which indicates that the data is not stationary in the variance, so the transformation is based on the value of λ obtained. The next step is testing stationarity in the mean by using ACF and PACF transformed tourist visit data as shown in Figure 2 and Figure 3.…”

Section: Methodsmentioning

confidence: 99%

“…Several previous studies related to forecasting the number of tourist visits have been carried out, among others: (Waluyo, 2019) in the Greater Bandung Tourism Area for the period June 2017 to December 2017 using the ARIMA model and the results obtained are 78.895% able to predict the number of tourist visits. Next (Hendayanti and Nurhidayati, 2020) in Bali for the period January 2019 to December 2019 the ARIMA model is 90% able to predict the number of tourist visits visiting these tourist attractions. However, this article involves a new element, namely intervention variables such as earthquakes and the COVID-19 pandemic which resulted in the low number of foreign tourist visits to West Nusa Tenggara.…”

Section: A Introductionmentioning

confidence: 99%

“…Out of sample data is used to predict or validate the accuracy of the data in an deepening process. So the emerging model is the best model from in-sample data (Hendayanti and Nurhidayati, 2020). In this study, the selection of the best model is done using the SSE and MSE values in the existing model then forecasting based on the best in-sample model.…”

Section: A Introductionmentioning

confidence: 99%

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Forecasting Foreign Tourist Visits to West Nusa Tenggara Using ARIMA Method

Soraya

Nurhidayati

Herawati

et al. 2021

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West Nusa Tenggara (NTB) is one of the provinces in Indonesia that has its own charm in the world of tourism and is known as a pioneer of halal tourism. In addition to domestic tourists, NTB tourism always has an attraction for foreign tourists. This is evidenced by the increasing number of foreign tourists visiting NTB from year to year before the Covid-19 pandemic. This condition certainly has a positive impact on increasing NTB’s economic growth in the tourism sector and indirectly on the optimization of existing infrastructure. The purpose of the study is to predict the number of foreign tourist visits to NTB so that it can assist the government in making decisions in preparing adequate facilities and infrastructure in the event of a surge in tourist visits. The method used in this study is the Box-Jenkins-ARIMA model. The ARIMA method is based on 3 models that are formed from the results of plot data. The data used in this study is secondary data sourced from the Central Statistics Agency (BPS) of West Nusa Tenggara (NTB), from January 2010 to June 2019. The results show that the ARIMA (4,1,1) model is the most widely used model. This model is suitable for predicting the number of foreign tourists visiting NTB because this model produces the lowest SSE and MSE values compared to other models.

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Section: Methodsmentioning

confidence: 99%

Section: A Introductionmentioning

confidence: 99%

Section: A Introductionmentioning

confidence: 99%

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Forecasting Foreign Tourist Visits to West Nusa Tenggara Using ARIMA Method

Soraya

Nurhidayati

Herawati

et al. 2021

Self Cite

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“…The results are different if the criteria used are AIC, AICu, AICc, BIC, HQ and HQc, where Models C2 and B2 are models based on these criteria. References [12] researched "The Application of Support Vector Regression (Svr) in Predicting the Number of Domestic Tourist Visits to Bali". The results of this study are based on forecasting using training data, the MAPE value obtained is 11, 34%, while using testing data, the MAPE value obtained is 7, 30%.…”

Section: ì 723mentioning

confidence: 99%

Community Purchase Decision Modeling in Bali with Non-Linier Methods

Hendayanti¹,

Nurhidayati

Soraya

et al. 2022

matrik

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The Covid-19 pandemic has resulted in all activities having to be carried out by implementing physical distancing or social distancing in accordance with health protocols for mutual safety. The government encourages people to do more activities at home, including shopping. Consumer perception of purchasing goods online is a process of evaluating various alternatives and choosing one alternative to purchase goods using internet media. The government appealed to the public to take advantage of online shopping to minimize the spread of Covid-19. This indicates that there are factors that influence consumer perceptions of purchasing goods online during the Covid-19 pandemic. The purpose of this study was to examine the effect of perceived convenience, perceived benefits, perceived trustworthiness, and product quality on people’s purchasing decisions in Bali using the Structural Equation Modeling-Partial Least Square (SEM-PLS) approach, Support Vector Regression (SVR), and Feed Forward Neural Network (FFNN). Based on the results of the tests carried out, the SEM-PLS model is able to produce a model with an R2 value of 72.7% with a MAPE of 337.37, an SVR model of 65.88% with a MAPE of 219.56 and a FFNN model of 97.28% with a MAPE of 90.22. Based on the resulting R2 and MAPE values, the FFNN model gives the highest results compared to other models.

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“…Sebagian besar penelitian peramalan jumlah kunjungan wisatawan mancanegara ke Indonesia sudah dilakukan, akan tetapi metode yang digunakan diantaranya: ARIMA [7][8]; Neural Network atau Jaringan Saraf Tiruan [9]; Holt-Winters [10]; SVR [11]. Sebagian besar menjelaskan membuat sistem peramalan kunjungan wisatawan sangat dibutuhkan untuk membantu dalam menyiapkan sarana dan prasarana, atau meningkatkan promosi, dan lainnya [12].…”

Section: Pendahuluanunclassified

Backpropagation untuk Memprediksi Jumlah Wisatawan Mancanegara ke Indonesia

Salim

Nafi’iyah²,

Mujilahwati

2021

SMATIKA

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Mengembangkan wilayah yang berpotensi wisata merupakan usaha meningkatkan sumber penghasilan bagi penduduk desa. Wilayah yang mempunyai area wisata dapat menjadi wahana yang menarik perhatian masyarakat, baik masyarakat dalam negeri maupun luar negeri. Wisatawan yang datang dapat memberikan pemasukan bagi daerah wisata ataupun masyarakat. Oleh karena itu dengan memprediksi jumlah wisatawan yang masuk dapat diprediksi berdasarkan data tahun-tahun sebelumnya. Tujuan melakukan prediksi agar dapat memperbaiki prasarana dan semua kebutuhan bagi para wisatawan. Tujuan penelitian ini menerapkan metode Backpropagation untuk memprediksi jumlah kunjungan wisatawan mancanegara ke Indonesia. Dataset yang digunakan dalam implementasi metode Backpropagation adalah 6000 baris, dan dibagi menjadi data training 4800 baris, dan data tes 1200 baris. Dataset diambil dari website bps, dengan variabel input adalah bulan, tahun, negara asal, pintu masuk wisatawan ke Indonesia, dan variabel output adalah jumlah wisatawan. Model dari Backpropagation dievaluasi dengan menghitung MAE, serta arsitektur yang dibangun adalah 4-9-1, 4 node input layer, 9 node hidden layer, dan 1 node output layer. Hasil ujicoba nilai MAE dari metode Backpropagation dalam memprediksi jumlah wisatawan ke Indonesia adalah 0,247.

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Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali

Cited by 9 publications

References 0 publications

Forecasting Foreign Tourist Visits to West Nusa Tenggara Using ARIMA Method

Forecasting Foreign Tourist Visits to West Nusa Tenggara Using ARIMA Method

Community Purchase Decision Modeling in Bali with Non-Linier Methods

Backpropagation untuk Memprediksi Jumlah Wisatawan Mancanegara ke Indonesia

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